Method and Apparatus for Generating Assessments Using Physical Activity and Biometric Parameters

ABSTRACT

The methods and apparatuses presented herein determine and/or improve the quality of one or more physiological assessment parameters, e.g., response-recovery rate, based on biometric signal(s) and/or motion signal(s) respectively output by one or more biometric and/or motion sensors. The disclosed methods and apparatuses also estimate a user&#39;s stride length based on a motion signal and a determined type of user motion, e.g., walking or running. The speed of the user may then be estimated based on the estimated stride length.

This application is a continuation of U.S. application Ser. No.16/516,845, which is a continuation of U.S. application Ser. No.16/221,012, filed 14 Dec. 2018, now U.S. patent Ser. No. 10/413,250,which is a continuation of U.S. application Ser. No. 15/690,940, filed30 Aug. 2017, now U.S. patent Ser. No. 10/206,627, which is acontinuation of U.S. application Ser. No. 15/120,766, filed 23 Aug.2016, now U.S. Pat. No. 9,788,794, which is the U.S. National Stage ofInternational Application No. PCT/US2015/018049, filed 27 Feb. 2015,which claims benefit of U.S. Provisional Application No. 61/945,960,filed 28 Feb. 2014, the disclosures of all of which are incorporated byreference herein in their entirety.

BACKGROUND

A personal health monitor provides a user with the ability to monitorhis overall health and fitness by enabling the user to monitor heartrate and/or other biometric information during exercise, athletictraining, rest, daily life activities, physical therapy, etc. Suchdevices are becoming increasingly popular as they become smaller andmore portable.

In addition to providing biometric information such as heart rate andbreathing rate, a personal health monitor may also provide physicalactivity information, e.g., duration, distance, cadence, etc. As withthe sensing or measurement of many parameters, however, the accuratedetermination of such information may be compromised by noise.

A user's cadence enables the user to monitor his current performancerelative to his personal goals, and therefore represents a particularlyuseful physical activity parameter. As used herein, a cadence representsthe number of repetitions per minute of some physical activity. Forexample, when the user is moving on foot, the cadence represents thenumber of foot repetitions or steps per minute. When the user is movingon wheels, the cadence represents the number of cycle repetitions (e.g.,crank or pedal revolutions) per minute.

Conventional devices may monitor the cycling cadence, for example, usinga cyclocomputer. A sensor system mounted to the crank arm and frame ofthe bicycle counts the number of pedal rotations per minute to determinethe cycling cadence. While such devices are useful and reasonablyaccurate, they are cumbersome and cannot easily be used with multiplebicycles. Further, such devices cannot provide an accurate estimate ofthe number of steps per minute taken, e.g., by a runner. Further still,such devices generally do not provide additional performanceinformation, e.g., calories burned, at least not with a desired degreeof accuracy.

In addition, there is an increased desire for everyday users to haveeasy access to information regarding their exercise routine and/or theresults of their exercise routine, regardless of the exercise conditionsor circumstances. For example, the speed of a runner jogging through aneighborhood may easily be determined by using a Global PositioningSystem (GPS). When the user jogs on a treadmill, however, GPS isuseless. In another example, it may be desired to determine a user'smaximum oxygen consumption/uptake/aerobic capacity (VO₂max) withoutsubjecting the user to costly and time consuming tests in a laboratoryset up for just such testing.

Thus, there remains a need for more portable devices wearable by a userand capable of accurately measuring and/or estimating biometric and/orphysical activity parameters (e.g., heart rate and/or speed), and anyassociated physiological assessment parameters (e.g., VO₂max), in a widevariety of scenarios.

SUMMARY

The solutions presented herein provide methods and apparatus for thedetermination and/or improved quality of one or more physiologicalassessment parameters based on biometric signal(s) and/or motionsignal(s) respectively output by one or more biometric and/or motionsensors. Exemplary biometric parameters include, but are not limited to,heart rate, breathing rate, breathing volume, blood pressure, pulsepressure, response-recovery interval, and heart rate variability.Exemplary physiological assessment parameters include, but are notlimited to, a user cadence and a user speed.

One exemplary method determines a physiological assessment parameterassociated with a user using an activity monitoring device comprising atleast one biometric sensor and at least one motion sensor, where atleast the biometric sensor contacts the user's skin. The methodcomprises processing a motion signal output by the motion sensor over afirst period of time to determine a physical activity parameter for theuser over the first period of time, and processing a biometric signaloutput by the biometric sensor over the first period of time todetermine a biometric parameter for the user over the first period oftime. The method further comprises determining the physiologicalassessment parameter based on the physical activity parameter and thebiometric parameter.

One exemplary apparatus comprises an assessment generation systemconfigured to determine a physiological assessment parameter associatedwith a user via at least one biometric sensor and at least one motionsensor comprised in an activity monitoring device disposed proximate theuser such that the biometric sensor contacts the user's skin. Theassessment generation system comprises a motion processing circuit, abiometric processing circuit, and an assessment processing circuit. Themotion processing circuit is configured to process a motion signaloutput by the motion sensor over a first period of time to determine aphysical activity parameter for the user over the first period of time.The biometric processing circuit is configured to process a biometricsignal output by the biometric sensor over the first period of time todetermine a biometric parameter for the user over the first period oftime. The assessment processing circuit is configured to determine thephysiological assessment parameter based on the physical activityparameter and the biometric parameter.

Another exemplary method estimates a speed of a user via a wearableactivity monitoring device disposed proximate the user and comprising amotion sensor. The method comprises processing a motion signal output bythe motion sensor to determine a type of user motion, and processing themotion signal based on the determined type of user motion to determine aphysical activity parameter. The method further comprises estimating astride length of the user based on the motion signal and the determinedtype of user motion, and estimating the speed of the user based on thephysical activity parameter and the stride length.

Another exemplary apparatus comprises an assessment processing circuitfor estimating a speed of a user via an activity monitoring devicedisposed proximate the user. The activity monitoring device comprises amotion sensor. The assessment processing circuit is configured toprocess a motion signal output by the motion sensor to determine a typeof user motion, and process the motion signal based on the determinedtype of user motion to determine a physical activity parameter. Theassessment processing circuit is further configured to estimate a stridelength of the user by processing the motion signal based on thedetermined type of user motion, and estimate the speed of the user basedon the physical activity parameter and the stride length.

Another exemplary method improves an accuracy of at least one of anestimated biometric parameter and an estimated physiological assessmentparameter determined for a current period of time and associated with auser wearing an activity monitoring device. The activity monitoringdevice comprises at least one motion sensor. The method comprisesgenerating a personalized biometric model for the user based on one ormore biometric parameters and one or more physical activity parametersdetermined for a second period of time previous to the current period oftime, and processing a motion signal output by the motion sensor todetermine a current physical activity parameter for the user for thecurrent period of time. The method further comprises processing thecurrent physical activity parameter based on the personalized biometricmodel to improve the accuracy of at least one of the estimated biometricparameter and the estimated physiological assessment parameterdetermined for the user for the current period of time.

Another exemplary apparatus comprises an assessment processing circuitconfigured for improving an accuracy of at least one of an estimatedbiometric parameter and an estimated physiological assessment parameterdetermined for a current period of time and associated with a userwearing an activity monitoring device. The activity monitoring devicecomprises at least one motion sensor operatively connected to theassessment processing circuit. The assessment processing circuit isconfigured to generate a personalized biometric model for the user basedon a history of one or more biometric parameters and one or morephysical activity parameters determined for a second period of timeprevious to the current period of time, and process a motion signaloutput by the motion sensor to determine a current physical activityparameter for the user for the current period of time. The assessmentprocessing circuit is further configured to process the current physicalactivity parameter based on the personalized biometric model to improvethe accuracy of at least one of the estimated biometric parameter andthe estimated physiological assessment parameter determined for the userfor the current period of time.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an exemplary cadence measurement system disposed in an earbud.

FIG. 2 shows a block diagram of an exemplary measurement system.

FIG. 3 shows an exemplary process for determining a parameter from dataprovided by a motion sensor.

FIG. 4 shows a block diagram of an exemplary parameter circuit.

FIG. 5 shows a more detailed process for determining a user cadence fromdata provided by a motion sensor according to one exemplary embodiment.

FIGS. 6A and 6B show simulation results associated with the disclosedsolution.

FIG. 7 shows a block diagram of an exemplary assessment system.

FIG. 8 shows simulation results associated with the assessment system ofFIG. 7.

FIG. 9 shows simulation results associated with the assessment system ofFIG. 7.

FIG. 10 shows simulation results associated with the assessment systemof FIG. 7.

FIG. 11 shows an exemplary process for obtaining the simulation resultsof FIG. 10.

FIGS. 12a and 12b show simulation results associated with the assessmentsystem of FIG. 7.

FIGS. 13a and 13b show exemplary details associated with generating amodel used by the assessment system of FIG. 7.

FIG. 14 shows optimal neural network details for exemplarysingle-representation models.

FIG. 15 show exemplary details associated with generating a model usedby the assessment system of FIG. 7.

FIG. 16 shows an exemplary process for estimating a user speed.

FIG. 17 shows an exemplary process for generating personalized activitysummaries.

FIG. 18 shows simulation results associated with the process of FIG. 17.

DETAILED DESCRIPTION

The measurement techniques and processors disclosed herein provide anaccurate measurement of one or more biometric, physical activity, and/orphysiological assessment parameters (e.g., values) based on a signalprovided by a motion sensor disposed proximate a user's body. As usedherein, the term “processor” broadly refers to a signal processingcircuit or computing system, or processing or computing method, whichmay be localized or distributed. For example, a localized signalprocessing circuit may comprise one or more signal processing circuitsor processing methods localized to a general location, such as to anactivity monitoring device. Examples of such monitoring devices maycomprise an earpiece, a headpiece, a finger clip, a toe clip, a limbband (such as an arm band or leg band), an ankle band, a wrist band, anose band, a sensor patch, or the like. Examples of a distributedprocessing circuit comprise “the cloud,” the internet, a remotedatabase, a remote processor computer, a plurality of remote processingcircuits or computers in communication with each other, etc., orprocessing methods distributed amongst one or more of these elements.The key difference between the distributed and localized processingcircuits is that a distributed processing circuit may includedelocalized elements, whereas a localized processing circuit may workindependently of a distributed processing system. Microprocessors,microcontrollers, or digital signal processing circuits represent a fewnon-limiting examples of signal processing circuits that may be found ina localized and/or distributed system.

The term “parameter” as used herein broadly refers to any set ofphysical properties, measured value(s), or processed information whosevalue(s) collectively relate the characteristics and/or behavior ofsomething. A “parameterization” refers to the generation of at least oneparameter from a set of data. For example, a biometric parameter, alsoreferred to herein as a physiological parameter, represents aphysiological function of an organism. Exemplary biometric parametersinclude, but are not limited to, a heart rate, breathing rate, breathingvolume, blood pressure, pulse pressure, R-R interval (time intervalbetween successive “R” peaks of a cardiac cycle, e.g., as shown in a QRScomplex of an electrocardiogram), heart rate variability, bodytemperature (e.g., core temperature, gut temperature, electrical orthermal conductivity in the body or skin of an organism, tympanictemperature, skin temperature, etc.), brain wave activity, energyexpenditure (e.g., calories burned), ECG activity, a parameterization ofsampled data from the at least one physiological sensor, or the like.

A physical activity parameter represents a parameter relating to aphysical activity of an organism. Exemplary physical activity parametersinclude, but are not limited to, a motion parameter e.g., a walkingcadence, running cadence, sprinting cadence, cycling cadence, limbcadence, walking speed, running speed, cycling speed, limb motion speed,head motion, a parameterization of sampled data from the at least onemotion sensor, or the like. Examples of a parameterization of digitallysampled data from a motion sensor (e.g., an accelerometer) include, butare not limited to, filtering (low-pass, high-pass, bandwidth, notch,etc.) of each accelerometer axis, processing the sampled data togenerate root-mean-squared (RMS) sampled data (e.g., squaring thesampled data from each accelerometer axis, summing the result, andtaking the square root of the sum), tracking the maximum values ofsampled data or processed sampled data over a period of time andaveraging the result, generating at least one spectral transform of thesampled data, identifying maximums or minimums in the sampled data,applying a function to the sampled data (e.g., a derivative function,integral function, trigonometric function, etc.).

As used herein, the “cadence” refers to the number of repetitions orcomplete cycles per unit of time, e.g., cycles per minute. Exemplarycadences include, but are not limited to, a step rate (e.g., the numberof steps or foot repetitions per minute), a cycle rate (e.g., the numberof pedaling cycles or cycle revolutions per minute), a repetition rate(e.g., with respect to lifting weights), etc. It will be appreciatedthat a step cadence may represent the user's cadence while walking,running, doing aerobics, climbing stairs, etc. Further, it will beappreciated that a cadence may refer to movement by mobile animals ormachinery, e.g., a walking robot. Exemplary animals include, but are notlimited to, biped animals (e.g., humans, birds, etc.) and quadrupedanimals (e.g., dogs, horses, etc.).

A physiological assessment parameter represents an assessment of abiometric/physiological function, e.g., the health or fitness level ofan organism and/or efficiency of a machine. Exemplary physiologicalassessment parameters include, but are not limited to, an assessment ofaerobic fitness level, VO2max, cardiovascular health, heart raterecovery time, endurance level, physical strength, exercise efficiency,running efficiency, biometric identification, a parameterization ofsampled data from at least at least two other parameters (biometricparameters and/or physical activity parameters, for example).

More broadly, a biometric parameter may include an acute (instantaneous)measurement of a biometric signal, such as an acute body temperaturemeasurement, and/or a processed collection of measurements of abiometric signal, e.g., measurements of heart rate over time byprocessing a PPG waveform over time. A physical activity parameter mayinclude an acute measurement of a motion signal, e.g., an acceleration,and/or or a processed collection of measurements of a motion signal,e.g., accelerometer values processed from multiple axes over a period oftime to identify the peak acceleration over a period of time. Aphysiological assessment parameter may include an acute measurement oftwo or more parameters (e.g., biometric parameters and/or physicalactivity parameters). For example, a physiological assessment parametermay include a measurement of a current user cadence and heart rate,and/or a processed collection of measurements of parameters over aperiod of time, e.g., a ratio of average cadence and average breathingrate generated over the course of a 30 minute treadmill run. In somecases, any one or more of these parameters may be the same as, orsubstantially the same as, a well-defined, well-established behavior,measurement, assessment, or classification. For example, a physiologicalassessment parameter that comprises a ratio of the change in heart rate(ΔH) and the change in time (Δt) over the course of 1-minute of restfollowing exercise is commonly referred to as “1-minute heart raterecovery.” In this case, the measured physiological assessment parameter(the ratio ΔH/Δt) is the same as the established physiologicalassessment (1-minute heart rate recovery).

It will be appreciated that a physiological assessment parameter may begenerated by processing physical activity parameter(s) and/or biometricparameter(s). For example, an assessment of a person's overall healthmay be determined by processing concurrent cadence (activity parameter)information and VO2max (physiological assessment parameter) information.More specifically, maintaining a relatively high (for one's owndemographic) VO2max over a period of time, with respect to one's averagedaily cadence, may be indicative of good overall health. Similarly, aperson may be identified from other persons with the aid of asufficiently unique VO2max/cadence pattern observed over a period oftime. The term “biometrics” refers broadly to a plurality of biometricparameters and/or physiological assessment parameters.

As used herein, the terms “inertia” or “inertial” refer broadly to“motion,” and an inertial sensor refers to a motion sensor.

FIG. 1 shows part of an exemplary measurement system 10, where one ormore sensors 14 are disposed in an ear bud 12, and a parameter circuit100 is operatively connected to the sensor(s) 14, e.g., via a wired orwireless connection. The parameter circuit 100 may be secured to theuser, e.g., via a clip. The ear bud 12 may comprise a wireless or wiredear bud that communicatively couples to a remote device (not shown),e.g., a music player, a smart phone, a personal data assistant, etc.While not required, it will be appreciated that the parameter circuit100 may be disposed in the remote device. While FIG. 1 shows thesensor(s) 14 as being part of an ear bud 12, it will be appreciated thatthe sensor(s) 14 may be disposed in any wearable device that secures tothe body of a user, e.g., a device that secures to an ear, finger, toe,limb, ankle, wrist, nose, etc. In some embodiments, the device maycomprise a patch, e.g., a bandage, designed to attach the system 10 toany desired location on the user's body. While FIG. 1 shows theparameter circuit 100 as being separate from the ear bud 12, it will beappreciated that part or all of the parameter circuit 100 may bedisposed in the ear bud 12

Measurement system 10 measures one or more parameters particular to theuser wearing the ear bud 12, e.g., biometric, physical activity, and/orphysiological assessment parameters. The measurement system 10 outputsthe measured parameter(s) to the user and/or to other processingfunctions or elements.

FIG. 2 shows a block diagram of an exemplary measurement system 10according to one exemplary embodiment. System 10 comprises the parametercircuit 100 coupled to one or more sensors 14 and an input/outputinterface 16, where the sensor(s) 14 include at least one motion sensor14 a, and an optional biometric sensor 14 b. It will be appreciated thatthe motion sensor 14 a may incorporate biometric sensor or physiologicalsensor capabilities. Motion sensor 14 a is configured to sense energy,e.g., motion, external to the system 10, and to output a motion signalS_(m) representative of the sensed energy, e.g., proportional to anacceleration of the user. The motion sensor 14 a may comprise a singleaxis sensor or a multiple axis sensor. Exemplary motion sensors 14 ainclude but are not limited to accelerometers, Micro-Electro-MechanicalSystem (MEMS) devices, gyroscopes, optical sensors, an opto-mechanicalsensor, a blocked channel sensor (e.g., as shown in US2010/0217102), acapacitive sensor, and a piezo sensor. When the motion sensor 14 acomprises a multiple axis sensor, frequency and power information fromeach axis may be combined or otherwise evaluated to determine thedesired information, e.g., the peak frequency and the motion power. Forexample, the spectral magnitude may be determined for each axis, where amaximum one of the spectral magnitudes, a sum of the squares, a maximumof the squares, a sum of the absolute values, a maximum of the absolutevalues, the root-sum-squares, the root-mean-squares, and/or thedecimation of the spectral magnitudes is ultimately used to determinethe motion power and to identify the peak frequency. Parameter circuit100 processes the motion signal S_(m) as disclosed herein to determineparameter(s) P. Input/output interface 16 provides input from the userto the parameter circuit 100, and outputs the determined parameter P. Itwill be appreciated that input/output interface 16 may include adisplay, a keyboard or other data entry device, and/or a transceiver fortransmitting the cadence to a remote device. Alternatively oradditionally, the input/output interface 16 may provide the cadence tothe display, a database, a processing circuit, and/or a processingfunction.

FIG. 3 shows an exemplary method 200 that may be implemented by themeasurement system 10 to determine a parameter P. After the parametercircuit 100 receives the motion signal S_(m) from the motion sensor 14 a(block 210), the parameter circuit 100 determines a peak frequency f_(p)based on S_(m) (block 220). The peak frequency f_(p) represents thefrequency component of the motion signal S_(m) having the largestamplitude. The parameter circuit 100 subsequently applies the peakfrequency f_(p) to one or more frequency threshold comparisons (block230). Based on the frequency threshold comparisons, the parametercircuit 100 determines the parameter(s) P, e.g., a user cadence C (block240).

FIG. 4 shows a block diagram of an exemplary parameter circuit 100configured to determine a user cadence C from the motion signal outputby the motion sensor 14 a. Parameter circuit 100 comprises a peakfrequency circuit 110, a frequency comparison circuit 120, and a cadenceprocessor circuit 130. Peak frequency circuit 110 determines the peakfrequency of the input motion signal. The frequency comparison circuit120 applies the peak frequency to one or more frequency thresholdcomparisons. The cadence processor circuit 130 determines the usercadence based on the peak frequency and the one or more frequencythreshold comparisons.

The peak frequency circuit 110 identifies the frequency component of themotion signal having the largest signal amplitude. In one exemplaryembodiment, peak frequency circuit 110 may achieve this goal byperforming a frequency transform of the motion signal to determine aspectral signal. The peak frequency circuit 110 then identifies thefrequency component of the spectral signal having the largest amplitudeas the peak frequency. It will be appreciated that other means, e.g.,phase-locked loop, pulse picking, or time-domain implementations, may beused to determine the peak frequency.

The frequency comparison circuit 120 applies the peak frequency to oneor more frequency threshold comparisons. The frequency peak oftencorresponds directly to the user cadence. However, in some instances,the user cadence is some harmonic factor of the peak frequency.Empirical research shows the peak frequency is often twice, half, orthee-halves the user cadence. As shown in FIG. 5, when sprinting ispossible the typical walking harmonics are 2 f_(p), 3/2f_(p), or ½f_(p)and the typical running harmonics are ½ f_(p). For example, harmonics at2 f_(p) and 3/2 f_(p) often occur when the user walks, but not when theuser runs. Thus, the cadence may actually be ½ f_(p) or ⅔ f_(p),respectively. When the user runs, harmonics at ½ f_(p) often occur.Thus, the cadence in this scenario may actually be 2 f_(p), ⅔ f_(p), or½ f_(p). Thus, the cadence circuit 100 must determine which harmonicfactor, if any, is applicable to determining the current user cadence.

The frequency threshold comparisons applied by the frequency comparisoncircuit 120 as disclosed herein solve this problem using one or morethreshold comparisons, where the thresholds are determined based on aprevious user cadence, a power of the motion signal, user activityparameters, user information, and/or empirical values. It will beappreciated that different harmonic factors and/or thresholds may applydepending on whether the user is sprinting, walking, running, ramping upfrom a low frequency value, cycling, etc. For example, harmonic factorsdue to arm swing, head bobbing, etc., impact the user cadencedifferently depending on how the user is moving, e.g., whether the useris running or walking. Thus, the cadence circuit 100 may optionallycomprise a power circuit 140, a power comparison circuit 150, a userinput circuit 160, a memory 170, and/or a threshold processor circuit180 that determine and/or provide the various harmonic factors andthresholds necessary to determine the user cadence.

The power circuit 140 is configured to determine the motion power (e.g.,the inertial power) p_(m) of the motion signal. To that end, the powercircuit 140 may compute p_(m) in the time domain, e.g., using the rootmean square, or in the frequency domain, e.g., using the amplitude of aspectral peak. The power comparison circuit compares p_(m) to a powerthreshold T_(m) to facilitate the determination of whether the user isrunning or walking. User input circuit 160 receives input from the user.The user input may be used to determine one or more user activityparameters, e.g., whether the user is on foot or on wheels, whethersprinting is possible, etc. Threshold processor circuit 180 isconfigured to determine one or more of the thresholds used by thefrequency comparison circuit 120, including any frequency thresholdsused to determine a running cadence, a walking cadence, a cyclingcadence, etc., and the power threshold used by the power comparisoncircuit 150. Memory 170 stores any predetermined thresholds, one or morepreviously determined cadences C_(p), the various harmonic factors usedby the cadence processor circuit 130, and any other information orsoftware necessary for successful operation of the parameter circuit100.

FIG. 5 shows an exemplary detailed process 300 executed by the parametercircuit 100 to determine the user cadence C. As shown by FIG. 5,parameter circuit 100 determines the user cadence based on the peakfrequency and one or more frequency threshold comparisons. In exemplaryembodiments, the parameter circuit 100 determines a user activityparameter, and determines the user cadence based on the frequencythreshold comparison(s) and the user activity parameter. For example,the physical activity parameter may identify whether the user is on footor on wheels (block 302). When on wheels, the frequency comparisoncircuit 120 compares the peak frequency f_(p) to a cycling thresholdT_(c), which may be fixed or variable based on a power of the motionsignal (block 310). When f_(p)<T_(c), the cadence processor circuit 130sets the cadence equal to the peak frequency (block 312). Otherwise, thecadence processor circuit 130 generates two or more test cadences, andsets the user cadence equal to the test cadence closest to a previoususer cadence (blocks 314-322). For example, the cadence processorcircuit 130 may generate three test cadences: C₁=½ f_(p), C₂=⅔ f_(p),and C₃=f_(p) (block 314), and compare the three test cadences to aprevious user cadence C_(p) (block 316). If C₁ is closer to C_(p) thanC₂ or C₃ are, the cadence processor circuit 130 sets the user cadenceequal to C₁ (block 318). If C₂ is closer to C_(p) than C₁ or C₃ are, thecadence processor circuit 130 sets the user cadence equal to C₂ (block320). If C₃ is closer to C_(p) than C₂ or C₁ are, the cadence processorcircuit 130 sets the user cadence equal to C₃ (block 322). While theexample of FIG. 5 shows determining and using three specific testcadences, it will be appreciated that any two or more test cadences maybe used.

When the user is on foot (block 302), the cadence processor circuit 130sets the user cadence equal to the peak frequency divided by a harmonicfactor, e.g., ½, 1, 3/2, 2, etc. More particularly, the cadenceprocessor circuit 130 determines the user cadence based on frequency andpower comparisons respectively performed by the frequency comparisoncircuit 120 and the power comparison circuit 150 (block 330). Forexample, when p_(m) is less than T_(m) and f_(p)≥T_(foot), cadenceprocessor circuit 130 generates two or more test cadences based on f_(p)and two or more of the harmonic factors, and determines the user cadencebased on the test cadences and a previous user cadence (blocks 360-368).For example, the cadence processor circuit 130 may generate three testcadences: C₁=½ f_(p), C₂=⅔ f_(p), and C₃=f_(p) (block 360), and comparethe three test cadences to a previous user cadence C_(p) (block 362). IfC₁ is closer to C_(p) than C₂ or C₃ are, the cadence processor circuit130 sets the user cadence equal to C₁ (block 364). If C₂ is closer toC_(p) than C₁ or C₃ are, the cadence processor circuit 130 sets the usercadence equal to C₂ (block 366). If C₃ is closer to C_(p) than C₂ or C₁are, the cadence processor circuit 130 sets the user cadence equal to C₃(block 368). While the example of FIG. 5 shows determining and usingthree specific test cadences, it will be appreciated that any two ormore test cadences may be used.

However, when p_(m)≥T_(m) and/or f_(p)<T_(foot), the cadence processorcircuit 130 determines the user cadence based on frequency thresholdcomparison(s) and a sprinting user activity parameter, which indicateswhether sprinting conditions are possible (blocks 332-356). Moreparticularly, when p_(m)≥T_(m) and/or f_(p)<T_(foot), the cadenceprocessor circuit 130 determines whether sprinting conditions arepossible based on user input (block 332). For example, the user mayselect an activity mode, e.g., walking, slow or low impact aerobics,high impact aerobics, running, etc. from a menu of options. Based on theselected activity mode, the cadence processor circuit 130 determineswhether sprinting conditions are possible. For example, when the userselects slow aerobics, the cadence processor circuit 130 determines thatsprinting is not possible. Alternatively, when the user selects running,the cadence processor circuit 130 determines that sprinting is possible.If sprinting conditions are possible, the cadence processor circuit 130determines the user cadence based on a comparison between f_(p) and alow frequency threshold T_(low) under sprinting conditions (blocks334-338). When f_(p)<T_(low), the cadence processor circuit 130 sets theuser cadence equal to the peak frequency divided by the ½ harmonicfactor, e.g., equal to twice the peak frequency (block 336). Otherwise,the cadence processor circuit 130 sets the user cadence equal to thepeak frequency (block 338).

If sprinting conditions are not possible, the cadence processor circuit130 determines the user cadence based on multiple frequency thresholdcomparisons under non-sprinting conditions (blocks 340-356). Moreparticularly, the cadence processor circuit applies the peak frequencyto multiple thresholds based on whether the peak frequency is ramping upfrom a low frequency value (block 340), and determines the user cadencebased on that ramping information and the frequency threshold conditions(blocks 342-356). While not required, in some exemplary embodiments, thelow frequency value is zero. During non-sprinting conditions when thepeak frequency is ramping up from a low frequency value, the cadenceprocessor circuit 130 sets the user cadence equal to the peak frequency(block 342).

However, during non-sprinting conditions when the peak frequency is notramping up from a low frequency value, the cadence processor circuit 130determines the user cadence based on multiple peak frequency thresholdcomparisons determined by the frequency comparison circuit 120 undernon-sprinting conditions relative to a low frequency threshold T_(low),an intermediate frequency threshold T_(med), and a high frequencythreshold T_(high), where T_(low)<T_(med)<T_(high) (blocks 344-356).More particularly, under these conditions when f_(p)<T_(low) (block344), the cadence processor circuit 130 sets the user cadence equal tothe peak frequency divided by the ½ harmonic factor, e.g., equal totwice the peak frequency (block 346). When f_(p)≥T_(low) andf_(p)>T_(high) (blocks 344 and 348), the cadence processor circuit 130sets the user cadence equal to the peak frequency divided by the 2harmonic factor, e.g., equal to half the peak frequency (block 350).When f_(p)≥T_(low) and f_(p)≤T_(high) and f_(p)>T_(med) (blocks 344,348, and 352), the cadence processor circuit 130 sets the user cadenceequal to the peak frequency divided by the 3/2 harmonic factor, e.g.,equal to two-thirds the peak frequency (block 354). Otherwise, thecadence processor circuit 130 sets the user cadence equal to the peakfrequency (block 356).

As discussed herein, parameter circuit 100 determines the user cadencebased on one or more frequency threshold comparisons. Each frequencythreshold, as well as the power threshold, may be determined empiricallyor based on one or more parameters, e.g., a previous user cadence, apower of a motion signal, user information, and/or a physical activityparameter. For example, the cycling threshold T_(c) and/or the footthreshold T_(foot) may be determined empirically based on observation,and/or based on user input information, user activity parameter, and/orp_(m). In one exemplary embodiment, for example, the foot threshold maybe determined according to:

$\begin{matrix}{T_{foot} = {120 + {40{\frac{p_{m}}{T_{m}}.}}}} & (1)\end{matrix}$

An exemplary cycling threshold T_(c) is 100 revolutions per minute,while an exemplary foot threshold T_(foot) is 145 steps per minute. Thepower threshold T_(m) and/or the low threshold may be determinedempirically and/or based on user information, e.g., the user's weight,shoe sole compliance information, etc., p_(m), a previous user cadence,and/or user activity parameters. In one exemplary embodiment, T_(low)=60(a constant). It has been shown, for example, that the low frequencythreshold is more accurate when determined as a function of p_(m). Forexample, when p_(m)≤T_(m), the low threshold may be determined based onp_(m) according to:

$\begin{matrix}{T_{low} = {60 + {20{\frac{p_{m}}{T_{m}}.}}}} & (2)\end{matrix}$

When p_(m)>T_(m), alternatively, T_(low) may be set equal to 80. Inanother exemplary embodiment, the low threshold may be determined basedon the previous user cadence according to:

T _(low)=0.6C _(p).  (3)

It will be appreciated that different values for T_(low) may be used fordifferent scenarios. Thus, a combination of the above-disclosed optionsmay be selectively used depending on the different scenarios, e.g.,whether p_(m)>T_(m). Similarly, the intermediate and high thresholds maybe determined based on a previous user cadence and/or p_(m). Forexample, the intermediate and high thresholds may be determined as afunction of the previous user cadence and a sprint factor. The sprintfactor for the intermediate threshold may be determined empirically,e.g., based on 1.75 or 1.4 times the previous user cadence. It will beappreciated that each threshold may be fixed or variable. It will alsobe appreciated that the frequency thresholds (e.g., T_(c), T_(foot),T_(low), T_(med), T_(high)) and the power threshold (T_(m)) discussedherein are exemplary and non-limiting; other thresholds may be useddepending on the system configuration, the information available to theparameter circuit 100, etc.

The user cadence method and apparatus disclosed herein accuratelydetermines a user cadence for a wide range of circumstances andenvironments. Further, because the user may wear the hardware necessaryto implement this solution, the solution disclosed herein is applicablefor any user activity, including cycling, walking, running, athletictraining, sports, aerobics, weight lifting or any other repetitiveexercises, jumping, etc.

FIGS. 6A and 6B show simulated results for one exemplary implementationof the measurement 10 system disclosed herein. The plots shown in FIGS.6A and 6B are generated from the same data set produced by an individualrunning and walking on a treadmill. FIG. 6A shows the user cadence withrespect to time as computed according to FIG. 5 using the spectral peakfrequency provided by the motion sensor 14 a. FIG. 6B shows the poweroutput by the motion sensor 14 a with respect to time. FIG. 6B alsoshows an exemplary power threshold T_(m) of 2000, which is used todetermine whether the user is running or walking/resting. The units forthe y-axis circuits FIG. 6B are “g's” scaled by a systematic multiplier,where 1g is the force of gravity on Earth at sea level. As shown byFIGS. 6A and 6B, the user is running from 125-215, seconds and from300-375 seconds. Thus, in these regions, user cadence method andapparatus disclosed herein avoids mistaking the peak frequencies above145 steps per minute as 2× or 3/2× harmonics. The 40-70 seconds regionshows 3/2× and ½× harmonics, the 80-120 seconds region shows 2× and ½×harmonics, and the 125-215 seconds region shows ½× harmonics. All ofthese harmonics, when divided by the corresponding harmonic factor asdisclosed herein, produce the correct user cadence.

In some embodiments, measurement system 10 may also comprise additionalsensors. For example, the measurement system 10 may include additionalbiometric sensors 14 b, e.g., blood flow (photoplethysmography (PPG)),body temperature, and/or heart rate sensors that contact at least someof the user's skin.

In some embodiments, the measurement system 10 may be part of anassessment generation system 700, e.g., as shown in FIG. 7 for thepurpose of generating physiological assessments of a user. Theassessment generation system 700 may comprise the sensors 14 andinput/output 16 of FIG. 2, as well as a signal extraction processingcircuit 710. The key purpose of the signal extraction processing circuitis to attenuate and/or remove unwanted noise from at least one of thesensors 14 and, optionally, to generate at least one accurate physicalactivity (motion) parameter and/or biometric parameter. The noiseattenuation and/or removal process may be passive or active, usingpassive and/or active filtering. In one embodiment, the signalextraction processing circuit 710 may comprise a motion processingcircuit 712, a biometric processing circuit 714, and a noise processingcircuit 716, e.g., as disclosed in FIG. 2 of WO 2013/109390 and FIG. 1of WO 2013/019494, both of which are incorporated herein by reference,and show additional details regarding signal extraction processingcircuit 710. In the illustrated embodiment, a motion processing circuit712 may be configured to process a motion signal output by the motionsensor(s) 14 a and/or a biometric signal output by the biometricsensor(s) 14 b. When configured to measure user cadence, the motionprocessing circuit 712 may comprise the parameter circuit 100. The noiseprocessing circuit 716 may be configured to remove or otherwiseattenuate cadence-related motion artifact noise from a biometric signaloutput by a biometric sensor 14 b and/or a biometric parameter output bythe biometric processing circuit 714, based, e.g., on the user cadenceoutput by the parameter circuit 100 (e.g., motion processing circuit712). Stated more broadly, the noise processing circuit 716 may beconfigured to remove or otherwise attenuate motion information from abiometric signal output by the biometric sensor 14 b and/or from abiometric parameter output by the biometric processing circuit 714. Forexample, a determined cadence frequency may be selectively (actively)removed from the frequency spectrum of the output signals of one or moreof the sensors 14 so that higher-quality output signals (“cleaneroutputs”) are achieved with substantially attenuated motion artifacts.As a specific example, the biometric sensor 14 b that outputs thebiometric signal may comprise a photoplethysmography (PPG) sensor, wherethe output biometric signal includes biometric information, e.g., heartrate and respiration rate information, as well as unwantedcadence-related information. The noise processing circuit 716 mayprocess, attenuate, or otherwise remove the unwanted cadence informationfrom the signals output by at least one biometric sensor 14 b,generating a cleaner biometric signal. Alternatively or additionally,the noise processing circuit 716 may facilitate selecting the propervalue of a biometric parameter that has been estimated by the biometricprocessing circuit 716. In one example, the noise processing circuit 716may comprise a signal processing circuit and/or processing method forgenerating a cleaner biometric signal and/or biometric parameter byattenuating, removing, and/or redacting frequencies associated with auser cadence (such as determined by the motion processing circuit 712).The noise processing circuit 716 may alternatively or additionallyincorporate passive filtering, e.g., analog or digital filters that arehigh-pass, low-pass, notch, bandpass, etc. It will be appreciated thatnoise processing circuit 716 may also process the motion signal and/orphysical activity parameters to generate a cleaner motion signal and/orcleaner physical activity parameters.

Once a cleaner biometric signal and/or cleaner biometric parameter isgenerated, the cleaner biometric parameter(s) and determined physicalactivity parameter(s) can be further processed to determine aphysiological assessment parameter of the user via an assessmentprocessing circuit (APC) 720. In general, APC 720 determinesphysiological assessment parameter(s) for a user wearing an activitymonitoring device, e.g., ear bud 12, wristband, armband, etc., byprocessing motion signal(s) to determine a physiological assessmentparameter. In one embodiment, APC 720 may process at least one activityparameter and at least one biometric parameter, each determined by thesignal extraction processing circuit 710, to determine at least onephysiological assessment parameter.

The general principle supporting the physiological assessmentmethodology implemented by APC 720 is that biometric signals may changein predictable manner due to a change in a user's activity level, andthis causal relationship may reflect a personalized biometric model. TheAPC 720 may be part of the assessment generation system 700 and mayemploy a personalized biometric model to generate at least onephysiological assessment parameter. In a specific embodiment, the APC720 generates at least one physiological assessment parameter based on adetermined user cadence and/or other physical activity parameters, andbiometric parameters and/or cleaner biometric parameter(s), andoptionally, based on the power p_(m) of the motion signal. To storemeasured data and information about the model employed by the APC 720,APC 720 may also have access to memory 170.

As an example of an embodiment of the assessment generation system 700and associated assessment generation method, the cleaner biometricparameter may comprise multiple biometric parameters, e.g., heart rate(HR), breathing rate (BR), R-R interval, blood oxygen information, etc.,such as provided by a PPG or pulse oximetry sensor when used as thebiometric sensor 14 b. The APC 720 may then generate the physiologicalassessment parameter(s) by combining the cleaner biometric parameterswith the physical activity parameters, e.g., a cleaner cadence.

As a specific example, the assessment processing circuit 720 maydetermine a start time and a stop time of a significant change in aphysical and/or biometric parameter, or the assessment processingcircuit 720 may generally identify a significant change in thesignals(s) output by the motion sensor 14 a and/or the biometric sensor14 b. A ratio may be generated between the biometric parameter and theassociated physical activity parameter. The ratio may be an acute,instantaneous ratio, e.g., heart rate divided by cadence, or an averageratio, e.g., average respiration rate divided by average cadence. Forexample, an average ratio may be generated by averaging multiplebiometric parameters over a selected period of time, averaging multiplephysical activity parameters, e.g., cadence, over that same period oftime, and dividing these average parameters to generate an averageratio. Alternatively, the change in a biometric sensor parameter over aperiod of time, e.g., caused by a change in the physical activityparameter(s), may be calculated relative to the change in the physicalactivity parameter(s) over the same period of time to generate adifferential ratio, e.g., (HRstop−HRstart)/(Cstop−Cstart)=ΔHR/ΔC. Ineither case, the ratios may then be compared with a personalized modelto generate a physiological assessment, e.g., of aerobic capacity, e.g.,VO2max, VO2, energy expenditure, recovery time, cardiovascular orcardiopulmonary functioning, etc.

For example, two or more users may be compared for physical fitness bycomparing their respective ΔHR/ΔC ratios. The user having a greaterreduction in heart rate with a given reduction in cadence over a fixedperiod in time may have a greater heart rate recovery rate than a userhaving a lower reduction in heart rate with the same reduction incadence over the same time period. Similarly, a ratio for average heartrate HRavg (or other biometric parameter) divided by average cadenceCavg can be mapped for a given user over a period of weeks. As the ratiodecreases over the course of physical fitness training, the decreasingratio may be indicative of improving physical fitness. The origin behindthis improved cardiovascular (aerobic) fitness level with a decreasedaverage ratio of (HRavg)/(Cavg) is that the decreased ratio indicatesthat less heart beats are required for the same motion of the user,which may be indicative of a more physically fit cardiovascular system.

However, in some cases, a plurality of users may be moving at the sameaverage cadence but one or more users may be generating less physicalpower due to a lower strike force during walking, running, cycling, etc.For this reason, a given ratio may be further normalized by the powerp_(m) (or average power p_(m,avg)) of the motion signal generated by themotion sensor 14 a. For example, for a given user, the average biometricparameter BSavg, measured over a period of time, divided by the averagecadence Cavg, measured over the same period of time, may be furthernormalized by the average power p_(m,avg) over the same period of timeto generate a more accurate average ratio that is more closely relatedto the user's overall fitness level. In this case, the fitness of a usermay be inversely proportional to BSavg/(Cavg*p_(m,avg)), where p_(m,avg)refers to an average motion power of the user over a given time period.It should be noted that this inverse relationship may hold true for manybiometric parameters, including but not limited to, HR, BR, bloodpressure, 1/R-R, and any other biometric parameters that increase withincreasing exercise intensity. This relationship is not likely to holdtrue for biometric parameters that do not necessarily increase withexercise intensity, and in some cases may actually decrease withexercise intensity, e.g., blood oxygen level. Stated another way,because HR, BR, blood pressure, and 1/R-R generally increase withincreasing exercise intensity, while their average values duringexercise generally decrease with increased physical fitness, the overallfitness of a user may be inversely proportional toBSavg/(Cavg*p_(m,avg)), In some cases, the ratio itself may not need tobe expressly calculated. For example, by detecting a change in cadenceover a period of time via one more elements of system 700, theassessment processing circuit 720 may calculate the recovery rate of abiometric parameter over that period of time, and this recovery ratevalue may be directly related to a user's overall fitness level.Moreover, the described ratio is not the only mathematicalrepresentation that may be used to relate overall fitness with one ormore concurrent biometric parameters or physical activity parameters.More generally, the overall fitness with increase with decreasing BSavg,and with increasing Cavg and p_(m,avg).

FIG. 8 shows an example of ΔHR/ΔC produced by the assessment generationsystem 700 to determine the fitness level of a user. In this example,the user wore a biometric headset according to the embodiments presentedherein, wherein the biometric sensor 14 b comprised a PPG sensor in theright earbud of the biometric headset and the motion sensor 14 acomprised a MEMS 3-axis accelerometer (also in the right earbud).Examples of such a headset may be found in U.S. Patent Publications2008/0146890, 2010/0217098, 2010/0217099, 2010/0217102, 2012/0197093,2013/0131519, and 2014/0012105, all of which are incorporated byreference herein. The processed (cleaner) heart rate measurement vs.time is shown as BPM (beats per minute) and the cadence measurement vs.time is shown as SPM (steps per minute). The assessment processingcircuit 720 determines the start and stop times (and thus the period oftime) of the analysis by detecting a substantial increase in cadence,e.g., an increase of more than 20 SPM, and by detecting a substantialdecrease in cadence, e.g., a decrease of more than 20 SPM. Because thestart and stop times can also be determined, various physiologicalassessment parameters can be determined, e.g., recovery rate. Forexample, the APC 720 calculates the 1-minute heart rate recovery byrecording the measured heart rate at the stopping point, recording themeasured heart rate at 1-minute past the stopping point, and subtractingthe latter from the former. In this example, the user had a 29 beatdecrease over 1 minute, and thus had a HRrec=30 BPM. The assessmentprocessing circuit 720 also calculates the average HR (HRavg) andaverage cadence (Cavg) over the period of time to calculate Havg/Cavg,which in this example was 1.02. Higher recovery rates and/or lowerratios are generally associated with better health/fitness.

In another example, assessment processing circuit 720 generates aphysical assessment parameter for a user based on data from multipleexercise sessions, as shown in FIG. 9. Data from multiple exercisesessions (e.g., runs) enables the assessment processing circuit 720 tomake determinations about some parameters, e.g., metabolic rate, withoutspecifically measuring such parameters. In this example, the user wore abiometric earbud, e.g., such as previously described in reference toFIG. 8, where the user ran without a cooling fan during Run 1 and with acooling fan during Run 2. A cooling fan is often used in treadmill runsto cool-down the skin and help evaporate sweat from the body. In FIG. 9,the measured heart rate vs. time is presented for the two exercisesessions where the user was running at the same speed (6 mph) for thesame duration of time (30 minutes). For the sake of simplicity, thecadence vs. time for each run is not shown. The assessment processingcircuit 720 identified the stopping point of the two runs by thedramatic drop in cadence of more than 20 SPM (signified by the dottedline). The assessment processing circuit 720 also measured the HRrecovery rates and the average ratios (HRavg/Cavg) for each run, asshown in FIG. 9. Because the user's HR recovery rate did notsignificantly change from run to run, while the user's average ratio didchange significantly, the assessment processing circuit 720 determinedthat the user was exercising at a higher body temperature during Run 1than during Run 2. The theory supporting this determination is that HRrecovery is more related to cardiac output, where the higher the heartrate recovery, the higher the cardiac output, and the greater theoverall physical fitness (cardiovascular fitness). However, the averageratio (HRavg/Cavg) is more closely related to overall acute (or current)stress on the heart muscle, which increases with body temperature. Theaverage ratio HRavg/Cavg may also be related to the overall physicalfitness of the user, but because the APC 720 is able to calculateessentially the same HR recovery rate for Run 1 and Run 2, theassessment processor is able to determine that the origin of the higheraverage ratio for Run 1 was due to a higher user running temperature.

The personalized model used by the assessment processing circuit 720 maycomprise an a priori model of a known relationship, a calibrated model,or a model generated over time based on a previously unknownrelationship using a history of the collected data. For example, thephysiological assessment parameters determined based on the Run 1 andRun 2 data shown in FIG. 9 were determined using an a priori model. Incontrast, a calibrated model may compare one or more of theaforementioned ratios (the estimated value) to the results of a standard(“benchmark”) fitness test (the actual value), e.g., a heart raterecovery test, VO2max test, submax test (such as a Rockport Test), orthe like. A calibration factor, relating the estimated versus actualvalue of the physiological assessment may then be generated for futureuse in predicting the physiological assessment using the calibrationfactor multiplied by the ratio. For example, the physiologicalassessment parameters shown in FIG. 9 can be tested against a benchmarktest of VO2maxactual, and the resulting relationships between theHRavg/Cavg and VO2maxactual and/or HR Recovery and VO2maxactual can bestored in memory 170 for future estimations of VO2maxestimated. As aspecific example of a calibrated model, consider FIG. 10, which plotsthe Measured VO2max (measured via gas-exchange analysis) of three usersvs. a measured relationship, e.g., a slope of HRrecovery vs. HRresponse.Namely, the HR and C of each user was measured with a PPG-basedbiometric armband sensor having the same biometric- andactivity-monitoring functionality as the biometric earbud shown in FIG.8. In this testing, however, data was collected for each user duringeveryday living conditions (free living), as opposed to a treadmill run,and periods of physical activity were defined as a user having anelevated cadence, e.g., a cadence higher than 100 steps/min, for acertain period of time. For each of the users, the APC 720 wasconfigured to identify targeted time periods, defined by an active timeperiod having at least 10 seconds of physical activity followed by arest period having at least 20 seconds of rest. For each targeted timeperiod, the APC 720 generates a linear relationship between the drop inheart rate over the rest period (HRrecovery) vs. the rise in heart rateover the active period (HRresponse), and generates a characteristicslope for this relationship. In one embodiment, this characteristicslope is generated by plotting, for each targeted time period, allmeasured HRrecovery values vs. all measured HRresponse values andgenerating an average slope for this relationship, such that thisaverage slope is the characteristic slope. Because the measured VO2maxof each user was measured in advance and stored in memory 170, thecharacteristic slope may be calibrated against the measured VO2max withthe linear relationship shown in FIG. 10. This yielded a calibratedestimate of VO2max based on the slope relating HRrecovery to HRrise.

FIG. 11 outlines a general method 400 for producing the results of FIG.10. Measured physiological assessment parameters are stored in memory170 (block 410). APC 720 identifies targeted time periods based on theuser cadence (block 420), e.g., by identifying active and rest periodsas indicated above. Subsequently, the APC 720 determines therelationship(s) between the recovery and response biometric parameters,e.g., HRrecovery and HRrise (block 430). The APC 720 may additionallymap the relationship(s) with the stored physiological assessmentparameter such that the relationship(s) may then be used at any latertimes to estimate future physiological assessment parameters withoutactually having to measure such physiological assessment parameters(block 440).

An example of a third type of model, e.g., the model generated over timebased on a learned relationship, is a model relating diet to overallphysical fitness. For example, a user recording their diet over severalweeks and exercising with system 700 will generate numerous datasetsthat may be used by the APC 720 to generate physiological assessmentparameters. If the user keeps track of their food intake with a digitalfood diary that is accessible to the APC 720, the APC 720 may alsocorrelate food intake over time with the aforementioned fitnessassessments (e.g., HR recovery, average ratio, VO2max, etc.) to generatea correlation between at least one food constituent and one of thefitness assessments. In this manner, an individual can generate apersonalized map of foods associated with higher or lower fitness forthat individual. The greater the granularity of ingredients recorded bythe food diary, the more specific the correlation may be between fitnesslevel and diet. Examples of potential food constituents include, but arenot limited to: calories, carbohydrates, fats, proteins, vitamins,minerals, sugars, and electrolyte (sodium, potassium, etc.) levels.Furthermore, further granularity may be provided by the types of thesedifferent constituents, such as the type of protein, vitamin, or salt,for example. Moreover, by combining the user's food diary informationwith biometric sensor readings from mastication or swallowing (asdescribed below), the APC 720 may generate a confidence indicator forthe user's manual input into the food diary. Using this technique, itmay be more challenging for the user to “fool” the APC 720 as itcalculates the correlation between diet and fitness.

In one embodiment, one or more elements of the system 10 may alsoidentify the context of the user's activity, e.g., a lower activitystate, by e.g., identifying a time period of substantially lower cadenceand/or substantially lower power p_(m). The time period associated withthe context determination may be the same as, different from, or overlapany other assessment time periods. This identification may in turnchange the contextual framework used by the APC 720. The APC 720 maythen process the cleaner biometric parameter(s) differently than duringa time period, e.g., a targeted time period, where cadence and/or poweris determined to be substantially higher, e.g., when there is a higheractivity state, such as a higher average cadence and/or motion power.For example, a change in the biometric parameter over a given time, inthe context of a lower activity state, may be indicative of aclassifiable biometric event. For example, a change in heart rate of1-15 BPM over 15-40 seconds may be associated with a swallowing event.In such case, the APC 720 may classify and count the number ofswallowing events over a period of time and generate an estimate for theduration of mastication. In contrast, if system 10 identifies a higheractivity state, the APC 720 may be redirected away from classifying andcounting swallowing events and towards identifying a physical fitnesslevel, e.g., using the aforementioned method. In this manner, thedetermination of a user's activity level can be used to change thecontext of the processing executed by APC 720, and thus change themethodology executed by the APC 720.

In some cases, the determined contextual framework can be used by theAPC 720 to change the “polling” (the electrical biasing and/or sampling)of the sensor(s) 14 a and/or 14 b. For example, the determined cadenceor motion power may be processed to change one or more sensor operatingparameters, e.g., the voltage, current, duty cycle, bias frequency orphase, bias waveform, biasing period, sampling time, samplingintegration period, sampling routine, etc., of an optical emitter and/ordetector in a PPG sensor embedded in a wearable device (such as theearbud of FIG. 1). Such processing may be beneficial for saving batterypower in the wearable device, as it may not be advantageous tocontinually bias a sensor when no useful biometric information isanticipated. As a specific example, consider the heart rate vs. timeplots of FIG. 8 and FIG. 9. The average heart rate can be effectivelyestimated during most of the run using just a few heart rate datapoints. However, many more data points are required to accurately tracethe rise time and fall time of the heart rate during the beginning andend of the run. In this example, APC 720 can be configured to detect arapid change in cadence and then initiate a control signal to the sensorto increase the polling (such as an increased bias frequency or period)to collect higher-acuity data during the period of time where the heartrate is most likely to substantially change. The sampling rate may alsoincrease. Alternatively, the sensor polling may normally be set to ahigh polling, and when APC 720 detects a steady cadence and/or steadyheart rate, the APC 720 may then send a signal to lower the sensorpolling. The time period for changing the sensor operating parameter(s)may be arbitrarily set in advance, based on an a priori model, e.g., bysetting the period from 10 seconds to 10 minutes, or may be determineduniquely for the user over a period of time. After the time period forchanging the sensor operating parameter(s), the sensor operatingparameter(s) may then return to the previous level (or some predefineddefault level). Though a specific example has been given for a PPGsensor, this variation also applies more broadly to any sensor in awearable device, e.g., an electrically biased sensor.

Another example of using a contextual framework for a physiologicalassessment is shown in FIG. 12, which presents the average breathingrate (BRavg) for a group of nine female users under two differentcontextual frameworks, where different diamonds in FIG. 12 indicatedifferent users. Each user had previously been evaluated viagas-exchange analysis to generate an objective (benchmark) measurementof their VO2max (measured VO2max). These users were each monitoredthroughout a variable treadmill run via a biometric headset (e.g., asdescribed earlier), with average speeds ranging from 2.46 mph to 4.4.mph. Each biometric headset was measuring heart rate, breathing rate,cadence, and other parameters in real time for each user as each userwas running. Using the methodology described herein, each user's speedwas calculated in real time by a processor, e.g., the signal extractionprocessor 710 and/or APC 720, factoring user cadence, stride length, andother parameters. During this time, the APC 720, for each headset,calculated the average breathing rate for the user and the estimatedVO2max of each user via a formula directly relating 1/BRavg to measuredVO2max. The formulas are shown in FIGS. 12a and 12b . FIG. 12b shows therelationship between each of the nine users when the breathing rate wasaveraged over each speed, with a contextual framework that does notdiscriminate between speeds. Note there is a not a significantrelationship (R2=0.186) between 1/BRavg and measured VO2max when thecontextual framework does not discriminate for a particular range ofspeeds. In contrast, FIG. 12a shows the relationship between each of thenine users when the breathing rate was averaged only over a period oftime where the average user speed was ˜2.3 mph. For this contextualframework, there is a strong relationship (R2=0.876) between 1/BRavg andmeasured VO2max. Thus, the APC 720 can be used to identify the user'sspeed (via user cadence and other parameters) and selectively averagethe breathing rate only for a time period where an average speed of 2.3mph or less is identified. The end result is an accurate estimate of theuser's VO2max factoring solely 1/BRavg. Note that the user's weight,height, and other parameters are not factored into this model ofestimated 1/BRavg vs. estimated VO2max, which exemplifies the robustnessand utility of this model.

FIG. 13 shows another example of a model generated over time based on alearned relationship via data pre-processing (data parameterization) andmachine learning. The “learned model” in this case is a learnedrelationship between measured blood pressure (as measured via a bloodpressure cuff) and the PPG signal (as output from a biometric earbud,e.g., shown in FIG. 8). In this particular study, the PPG sensor 14 bwas configured to detect blood flow changes from the ear region, betweenthe anti-tragus and concha of the ear, e.g., as described in U.S. Pat.Nos. 8,647,270 and 8,700,111. For this model, several users were studiedwearing a biometric sensor while also wearing a blood pressure cuff tomeasure blood pressure. In this study, however, the APC 720 wasconfigured to execute the following:

1) Determine whether the user cadence was below a threshold (˜100steps/minute) in order to assure data integrity of the PPG signal.

2) If so, high-pass filter the PPG signal to remove (or attenuate) DCand low-frequency components.

3) Buffer numerous pulses of data (e.g., at least ten complete signals)and identify the beginning and end points of each signal in the timedomain.

4) Create a spline for each signal across a finer mesh of constantsize—in this case 500 samples—so that the signals are effectivelynormalized in time to eliminate pulse rate dependence.

5) Average the splines to produce a single “average spline” signalrepresenting the average pulse shape.

6) Normalize the amplitude of the average spline signal—normalizedbetween 0 and 1—across a time axis of 500 points for one wave period(e.g., normalizing and discretizing over 500 points). While thisparticular study used 500 points, later studies have shown that a 4×decimation of data points did not degrade blood pressure model accuracy.This suggests that 125 points, and perhaps even fewer points, may besufficient to maintain model accuracy.

7) Differentiate and integrate the average spline signal to providemultiple representations of the data to the machine learning tool.

The data resulting from this processing as executed by the APC 720 wasthen analyzed outside of the APC 720 using a machine learning tool. Theresulting model was later added to the APC 720 “tool box” to enable thegeneration of a blood pressure assessment. To summarize the machinelearning methodology, seventy-two datasets were available for themachine learning tool, with data comprising both raw PPG data from thebiometric sensor and measured blood pressure data from a standard bloodpressure cuff. The mean values for systolic BP, diastolic BP, and pulsepressure were 129.0±9.3, 83.6±8.0, and 45.5±9.0 mmHg, respectively witha mean heart rate of 73.8±9.4 BPM. These datasets were divided into 48sets for model development and 24 sets for model validation. The neuralnetwork consisted of a set of inputs (the input layer), a set of outputs(the output layer), and a set of hidden layers. Three networks werebuilt, each with multiple input nodes (FIG. 14) and a single node outputlayer: systolic pressure, diastolic pressure, or pulse pressure. Theinput layer consisted of a set from the available waveformrepresentations shown in FIG. 13. The representations (each normalizedand discretized as described earlier) were called (i) “wave” for thenormalized PPG signal, (ii) “first derivative”, (iii) “secondderivative”, and (iv) integral. Each representation was usedindividually, as well as in combination with other representations. Thedata was explored by running many trials with various combinations ofthe available input datasets paired with a given output layer.Experiments examined different hidden layer topologies and learningstrategies. Once an optimized model was developed for the trainingdataset by the machine learning tool, the model was applied to thevalidation dataset, and a summary of the results of the model ispresented in FIG. 15. In FIG. 15, the closer a particular value is to“1,” the better that waveform is at predicting the correspondingparameter. The optimized model itself is embodied by a myriad ofcoefficients that relate inputs to outputs; the number of coefficientsin the resulting model was ˜7800 for systolic pressure, ˜15,700 fordiastolic pressure, and ˜23,000 for pulse pressure (FIG. 14). Ofinteresting note, the best representation (defined by the bestcorrelation coefficient for the model against the validation data) foreach output layer type was different. To predict systolic bloodpressure, e.g., the wave (e.g., the pulse wave, also known as the PPGwaveform) provided a better correlation than any other individualrepresentation. However, the “wave” was the poorest predictor ofdiastolic blood pressure, the integral representation provided the bestcorrelation for predicting diastolic blood pressure, and the firstderivative provided the best predictor for pulse pressure. In principle,the models of FIG. 14 are “single representation” models, as they employcoefficients relating to a single transform only (e.g., integrals only,pulse waves only, first derivatives only, etc.). However, additionalmodels were explored, factoring the “best of two” transforms. The lastcolumn of FIG. 15 shows the results. In the case of systolic anddiastolic pressure, e.g., slightly better correlational coefficientswere observed by factoring the best two transforms. For example, inputnodes comprising both the second derivative and integral of the PPGwaveform resulted in a better correlation for the diastolic pressurethan did the integral alone. The resulting optimized model was thenincorporated into the APC 720. It should be noted that an assessment ofuser cadence was critical for autonomous operation of this bloodpressure (BP) assessment method, as the method is sensitive to the shapeof the PPG signal. It should further be noted that otherparameterizations of the PPG signal may be used with this solution,e.g., higher-order derivatives (3rd, 4th, etc.), higher-ordertime-integrals, spectral representations of the signal(s), spectralrepresentations of the derivatives or integrals of the signal(s), andthe like. It should also be noted that although the experiment behindFIGS. 13 and 15 was based on PPG measurements from the ear, theembodiments disclosed herein are not limited to this region of the bodyand, rather, translates to any region of the body where sufficient bloodflow modulations can be measured, e.g., the wrist, arm, leg, digits(e.g., fingers and/or toes), nose, head (e.g., forehead, face, temple,ear region, etc.), neck, torso, and other regions of the body. However,the choice of location of the PPG sensor 14 b may change the shape ofthe collected PPG waveform, and thus, the coefficients relating thebiometric parameters (e.g., the “pulse wave” shape, “first derivative,”“second derivative,” and integral of the normalized PPG signal) to bloodpressure may be different. For this reason, it is generally recommendedthat the circuit, e.g., the APC 720, be retrained each time the PPGsensor 14 b is place on a different body part or each time the PPGoptomechanics are changed. Lastly, while the experiment behind FIGS. 13and 15 resulted in mathematical relationships applying broadly tomultiple individuals, the same solution may be applied towards a singleindividual, with “training” datasets feeding into the machine learningtool that are associated with one person only (and not a group ofpersons). In such cases, the accuracy of the resulting blood pressuremodel may be higher for a single individual. However, the resulting“individual” algorithm may not be scalable to a broad population ofsubjects, and the APC 720 may need to be recalibrated (against a knownblood pressure benchmark, e.g., as provided by a conventional bloodpressure cuff) for each user wearing the device.

The previous discussions focused on determining various physiologicalassessment parameters based on biometric and motion signals. In anotherembodiment, the speed of a user may be estimated based on the motionsignal output by a motion sensor disposed proximate the user. In thisembodiment, the estimated speed is determined using an estimated stridelength (as opposed to an actual measurement of the stride length) of theuser. FIG. 15 shows one exemplary process 500 implemented by the APC 720to estimate the user's speed. The APC 720 processes a motion signaloutput by the motion sensor to determine a type of motion (block 510).Exemplary types of motion include but are not limited to, resting,walking, running, and sprinting. APC 720 then processes the motionsignal based on the determined type of user motion to determine one ormore physical activity parameters and to estimate a stride length of theuser (block 520). Subsequently, the APC 720 estimates the speed of theuser based on the determined physical activity parameter and stridelength (block 530).

In one exemplary embodiment, the physical activity parameter(s) includethe user cadence when the type of motion is walking or running. Forexample, the APC 720 estimates the speed based on a user cadence and theestimated stride length when the user is walking or running. In anotherembodiment, APC 720 determines the stride length (used to estimate thespeed) based on a user cadence and at least one non-cadence activityparameter when the type of motion comprises a running motion. When thetype of motion comprises a walking motion, the APC 720 determines thestride length based on the user cadence and at least two non-cadenceactivity parameters. For example, if the identified type of motion isrunning or walking, APC 720 determines they user speed (e.g., inmeters/minute) according to:

Cl*SL*C,  (4)

where C represents the running or walking cadence, SL represents theestimated stride length, and Cl=1 when the speed is given inmeters/minute. For this example, a running stride length SL_(R) may becalculated according to:

SL_(R) =C2+C3*C _(R) *AP1  (5)

when the identified type of motion is a running motion, where C_(R)represents the running cadence, C2 and C3 represent experimentallyderived constants, and AP1 represents a physical activity parameter orparameterization. Alternatively, a walking stride length SL_(W) may becalculated according to:

SL_(W) =C4+C5*AP1+C6*AP2+C7*C _(W) *AP1+C8*C _(W) *AP2,  (6)

when the identified type of motion is a walking motion, where C_(W)represents the walking cadence, C4 through C8 represent experimentallyderived constants, AP1 represents a physical activity parameter orparameterization, and AP2 represents an additional physical activityparameter or parameterization. The values of the experimentally derivedconstants will depend strongly on the type of motion sensor used togenerate the cadence. For example, when the motion sensor is anaccelerometer, the constants may depend on the type of accelerometerused. These constants can be generated by measuring the actual stridelength of the user or group of users with a ruler, collecting theaccelerometer outputs, and solving for the aforementioned equationsempirically for a known cadence, predetermined AP1, and predeterminedAP2. The physiological reasoning behind a more complicated formula(requiring multiple physical activity parameters or parameterizations)for stride length during walking (versus running) may be explained bythe fact that running comprises a more uniform acceleration pattern fora broad user group when compared with walking, which is characterized bymore individual variation. Namely, the accelerometer outputs for runningare more similar for a mass population than the accelerometer outputsfor walking in a mass population. Stated another way, there are moreways to successfully walk than there are to successfully run, and theaccelerometer outputs for walking contain motion information due to avariety of body motions (not necessarily associated with cadence) thatare not typically seen during running. Additional improvements in thestride length estimation may be generated by adding additionalparameterizations of the activity (such as additional parameterizationsof an accelerometer output signal), e.g., adding AP3, AP4, AP5, etc.

For the case of measuring user speed during cycling, stride length maynot be relevant. And the circumference of the bicycle tires may also notbe very useful without context of the gear setting. Thus, to measurespeed during cycling, the APC 720 may first determine that the user iscycling (using methods described earlier). Then the APC 720 may definethe measured cadence as a cycling cadence. The acceleration may also bemeasured by an accelerometer 14 a, which may also be providing themotion signal for processing the cadence. With the cycling cadence andacceleration known, the APC 720 may then estimate the speed of the userduring cycling. This estimation may comprise a look-up table for mappingcadence and/or acceleration values with user speed. APC 720 may alsoestimate user speed by estimating the gear setting required for a givencadence and acceleration value. Other estimation methods may be employedfor speed as a function of user cadence and/or acceleration.Alternatively, the APC 720 may not estimate speed but may rathergenerate a dimensionless value that is a function of user cadence andacceleration and then send this dimensionless value to a remote device(e.g., a smartphone, sport computer, cycling computer, smartwatch, etc.)for estimating speed via algorithms (e.g., calibration algorithms) onthe remote device.

The assessment generation system 700 of FIG. 7 may also be used togenerate personalized activity summaries based on user biometric dataand user cadence. FIG. 17 shows an exemplary method 600 for generatingsuch personalized activity summaries. After sensing data from at leastone biometric sensor 14 b and at least one motion sensor 14 a andprocessing the sensed data to generate at least one activity parameterand at least one biometric parameter (block 610), the system 700assesses the activity to identify and/or quantify the activity (block620). The system 700 then determines the cadence of the identifiedactivity (block 630). In one exemplary embodiment, the APC 720 mayprocess the physical activity parameter of the peak motion frequency anddetermine the cadence of the activity based on some multiple of the peakfrequency that correlates best with the identified activity. The systemmay then process biometric data (e.g., one or more biometric parameters)and cadence data over a period of time to estimate the intensity of theactivity, (block 640), and store and/or communicate a summary of theactivity (block 650). FIG. 18 shows data from a real-life example ofthis system 700 in action. In particular, FIG. 18 shows a graph ofactions/minute (e.g., a heart rate beats per minute and a cadence) vs.time (in minutes) for a user wearing a PPG-based heart rate monitor atthe ear. In the example of FIG. 18, the user was performing “dips” (atriceps exercise). By processing sensor readings from the sensors 14 a,14 b, the system 700 was able to generate time-dependent heart rate andactivity information. In this particular example, the APC 720 identifiedthat the activity was not jogging or cycling, e.g., by using thresholdcomparisons (as described earlier), and so the APC 720 interpreted thetime-dependent accelerometer signals as a cadence of exerciserepetitions, e.g., “dip” repetitions, and not footsteps. Thisdistinction is important, as the peak frequency of the motion signalassociated with a repetition exercise may be a multiple of the actualcadence, depending on how the motion signal is processed, and in somecases the cadence may be a multiple of the peak frequency of the motionsignal. For example, if the compression and relaxation cycles generatesimilar motion signals, then the peak frequency of the motion signal maybe twice that of the actual exercise cadence. In this case, the system700 divides the peak frequency by two to determine the true cadence.After identifying the true cadence of the activity, the data may bestored and/or presented to the user, e.g., via a visual display (asshown in FIG. 18). Further, by counting the number of peaks in the heartrate signal, the APC 720 is able to determine the number of sets (thenumber of times the user began a period of repetitions). In the exampleof FIG. 18, there are three peaks in the heart rate signal, and thus theuser did three sets. Also, the APC 720 may determine the number ofrepetitions by, e.g., integrating the time-dependent cadence readingover time. It should also be noted that the intensity of each set (interms of user energy expenditure per set) may be approximated byfactoring the heart rate, cadence, and/or a function of the heart rateand cadence for each set. One exemplary method determines the intensityof each set by integrating the heart rate and the step rate over time,and multiplying the products of these two integrals for each set.Another exemplary method combines these two integrals into a regressionequation, where each integral is multiplied by a coefficient and theproducts are summed together and correlated with an intensity benchmarksensor (e.g., gas-exchange analysis used in an indirect calorimeter).

A few important points should be mentioned about the method 600 of FIG.17. First, the method may optionally employ user input regarding thephysical activity performed. The system 700 may use this user input todirectly determine the activity being performed by the user, or tofacilitate the determination of the activity being performed (block620). As noted herein, the user input is not required, but it mayimprove the accuracy of the activity interpretation, and thus mayimprove the accuracy of the cadence determination since the appropriatemotion frequency-to-cadence multiplier (e.g., ½× 1×, 3/2×, 2×, etc.) maybe determined, as previously described. For example, there may be a userinterface (audible, touch-based, visual, etc.) for the user tocommunicate the type of activity being performed. In a specific example,the user may vocalize the activity being performed via a smart device,e.g., an audio headset (as in the particular example of FIG. 18, wherethe audio headset and sensor(s) 14 are integrated together) or a mobileor fixed remote device (e.g., a wearable computer, digital music player,mobile phone, router, cloud server, camera, etc.), that is in wired orwireless communication with the wearable sensor(s) 14. The smart devicemay comprise voice recognition algorithms for processing vocalinformation and interpreting that the user is beginning to perform anexercise. Similarly, the user may input the activity information into amobile device via a touchscreen or the like.

Second, although the described method 600 was exemplified with an earbudused as the sensor housing form-factor, the described embodimentsbroadly apply to any wearable form-factor (e.g., wristbands, armbands,leg bands, rings, jewelry, clothing, headbands, patches, smart tattoos,etc.) that comprise a biometric sensor 14 b and a motion sensor 14 a(though the embodiments described herein are especially suited for anysensor employing both PPG and accelerometry). The key difference withdifferent form-factors is that the peak motion frequency-to-cadenceconversion ratio may be different depending on the location on the bodywhere the user is wearing the device, as different harmonics may beintroduced into the motion signal depending on the location of themotion sensor 14 a on the body.

Third, the processing steps may be performed by the processing circuit710, the APC 720, or a combination of both (e.g., via distributedprocessing). In the particular example of FIG. 18, the system 700 wasset in a “running” mode,” such that the peak frequency was originallyassumed to be a running cadence. The processing circuit 710 on the PPGearbud generated the heart rate and “running cadence” information, butthe APC 720 determined the physical activity type (e.g., dips) viaprocessing the signal output by the motion sensor 14 a, and then appliedthe appropriate multiplier to the “running cadence” frequency (e.g., thepeak motion frequency) in order to determine the correct exercisecadence, e.g., the dips cadence. However, in some embodiments, theprocessing step for determining a cadence value may follow theprocessing step for determining the activity type. Also, in someembodiments, either the processing circuit 710 or the APC 720 mayexecute the entire method 600 alone, without distributing the processingsteps.

Various elements disclosed herein are described as some kind of circuit,e.g., a parameter circuit, peak frequency circuit, frequency comparisoncircuit, cadence processor circuit, power circuit, power comparisoncircuit, user input circuit, threshold processor circuit, motionprocessing circuit, biometric processing circuit, noise processingcircuit, assessment processing circuit, etc. Each of these circuits maybe embodied in hardware and/or in software (including firmware, residentsoftware, microcode, etc.) executed on a controller or processor,including an application specific integrated circuit (ASIC). Further,while the figures show these circuits as being separate circuitsoperating in communication with each other, one or more of the circuits,e.g., the motion processing circuit, biometric processing, circuit, andnoise processing circuit, may be implemented on a single circuit, e.g.,a single microprocessor circuit.

The present invention may, of course, be carried out in other ways thanthose specifically set forth herein without departing from essentialcharacteristics of the invention. The present embodiments are to beconsidered in all respects as illustrative and not restrictive, and allchanges coming within the meaning and equivalency range of the appendedclaims are intended to be embraced therein.

What is claimed is:
 1. A wearable device configured to assess subjectblood pressure, the wearable device comprising: a photoplethysmography(PPG) sensor; an inertial sensor configured to sense subject motion; anassessment processor operatively connected to the PPG sensor and theinertial sensor, the assessment processor being configured to: processinertial signals output by the inertial sensor to determine a dataintegrity of a plurality of PPG waveforms output by the PPG sensor; andresponsive to the determined data integrity, process the plurality ofPPG waveforms using a neural network comprising thousands ofcoefficients to generate an assessment of the subject blood pressure. 2.The wearable device of claim 1, wherein the wearable device comprises adevice worn at the ear.
 3. The wearable device of claim 1, wherein thewearable device comprises an audio earbud.
 4. The wearable device ofclaim 3, wherein the PPG sensor is configured to detect blood flowchanges between an anti-tragus and a concha of an ear of the subject. 5.The wearable device of claim 1, wherein the assessment processorprocesses the plurality of PPG waveforms by conditionally processing theplurality of PPG waveforms when the determined data integrity assures anintegrity of the plurality of PPG waveforms.
 6. The wearable device ofclaim 1, wherein the assessment processor is further configured tochange a polling of the PPG sensor responsive to the assessmentprocessor detecting a steady subject cadence from the inertial signalsoutput by the inertial sensor and/or a steady subject heart rate fromthe PPG waveforms output by the PPG sensor.
 7. The wearable device ofclaim 1, wherein the assessment processor is further configured torecalibrate the neural network for a specific subject.
 8. The wearabledevice of claim 1, wherein the assessment processor is furtherconfigured to change a sampling rate of the PPG sensor responsive to theassessment processor detecting a steady subject cadence from theinertial signals output by the inertial sensor and/or detecting a steadysubject heart rate from the PPG waveforms output by the PPG sensor. 9.The wearable device of claim 1, wherein the wearable device comprises adevice worn at the wrist, arm, let, digits, nose, head, neck, or torso.10. A method of assessing subject blood pressure via wearable device,the method comprising: collecting a plurality of photoplethysmography(PPG) waveforms from a PPG sensor in the wearable device; collectinginertial data associated with subject motion from an inertial sensor inthe wearable device; process the inertial data in an assessmentprocessor operatively connected to the PPG sensor and the inertialsensor to determine a data integrity of the plurality of PPG waveforms;and responsive to the determined data integrity, processing theplurality of PPG waveforms in the assessment processor using a neuralnetwork comprising thousands of coefficients to generate an assessmentof the subject blood pressure.
 11. The method of claim 10, wherein thewearable device comprises a device worn at the ear of the subject, andwherein the collecting the plurality of PPG waveforms comprisescollecting the plurality of PPG waveforms from the ear of the subject.12. The method of claim 10, wherein the collecting the plurality of PPGwaveforms comprises collecting the plurality of PPG waveforms bydetecting blood flow changes between an anti-tragus and a concha of anear of the subject.
 13. The method of claim 10, wherein the processingof the plurality of PPG waveforms comprises conditionally processing theplurality of PPG waveforms when the determined data integrity assures anintegrity of the plurality of PPG waveforms.
 14. The method of claim 10,further comprising the assessment processor changing a polling of thePPG sensor responsive to a detection, by the assessment processor, of asteady subject cadence from the inertial data and/or of a steady subjectheart rate from the plurality of PPG waveforms.
 15. The method of claim10, further comprising the assessment processor recalibrating the neuralnetwork for a specific subject.
 16. The method of claim 10, furthercomprising the assessment processor: buffering the plurality of PPGwaveforms; and generating a plurality of representations of theplurality of PPG waveforms; wherein the processing of the plurality ofPPG waveforms comprises processing, by the assessment processor, theplurality of representations of the plurality of PPG waveforms using theneural network comprising thousands of coefficients to generate theassessment of the subject blood pressure.
 17. The method of claim 16,wherein the generating the plurality of representations comprisescomputing a derivative of at least one of the splines of the pluralityof PPG waveforms to generate at least one of the plurality ofrepresentations.
 18. The method of claim 16, wherein the generating theplurality of representations comprises computing an integral of at leastone of the splines of the plurality of PPG waveforms to generate atleast one of the plurality of representations.
 19. The method of claim16, wherein at least one of the plurality of representations comprises aspectral representation of at least one of the plurality of PPGwaveforms.
 20. The method of claim 14 further comprising the assessmentprocessor creating a spline for each of the plurality of PPG waveforms,wherein the generating the plurality of representations of the pluralityof PPG waveforms comprises generating the plurality of representationsof the plurality of PPG waveforms from the splines.
 21. The method ofclaim 10, further comprising determining a spectral representation of atleast one of the PPG waveforms, wherein the processing of the pluralityof PPG waveforms comprises, responsive to the determined data integrity,processing in the assessment processor the spectral representation usingthe neural network to generate the assessment of the subject bloodpressure.
 22. The method of claim 10, further comprising the assessmentprocessor changing a sampling rate of the PPG sensor responsive to theassessment processor detecting a steady subject cadence from theinertial signals output by the inertial sensor and/or detecting a steadysubject heart rate from the PPG waveforms output by the PPG sensor. 23.The method of claim 1, wherein the wearable device comprises a deviceworn at the wrist, arm, let, digits, nose, head, neck, or torso, andwherein the collecting of the plurality of PPG waveforms comprisescollecting the plurality of PPG waveforms from the wrist, arm, leg,digits, nose, head, neck, or torso of the subject.